1. Technical Field
This disclosure relates to optical recognition methods and systems and more particularly, to an intelligent camera for character recognition.
2. Description of the Related Art
Most image processing systems for industrial applications and video surveillance are still based on a personal computer (PC), a frame grabber and a separate CCD camera. PC based vision systems for traffic surveillance have been described in the prior art. In contrast to PC based systems intelligent or smart cameras are recently becoming more popular, since they offer several advantages. They require minimal space, because processor and sensor are integrated in one package. They are more robust and reliable in outdoor environments, they require less maintenance and they are well suited for cost sensitive applications. However, intelligent cameras offer less computational power, since they are typically one or two generations behind the current processor generation and they are limited with respect to main memory (e.g., 8 MB) and hard disk space (e.g., a 5 MB flash disk).
In principle, license plate recognition is may be similar to optical character recognition (OCR) for document processing. However, existing OCR engines cannot be successfully used as license plate readers because they cannot tolerate an extreme range of illumination variations, such as inhomogeneous illumination and shadows, blurring due to dirt, screws, particles, etc. In addition, OCR products are limited due of their memory and processing speed requirements.
Therefore, a need exists for versatile algorithms for applications that go beyond simple identification or measuring tasks. A further need exists for a system that reliably recognizes license plates for applications ranging from surveillance to automated systems for determination of the parking fees.
A method for recognizing license plates employing an intelligent camera with a processor and a memory, in accordance with the present invention, includes capturing an image including a license plate by the intelligent camera, and detecting a region in which the license plate is located by performing a coarse localization of the image. Orientation, position, and illumination conditions of the image are detected and accounted for to obtain a baseline image of the license plate. A fine localization of the baseline image is performed to obtain a more accurate depiction of vertical resolution of the baseline image of the license plate. Characters depicted in the baseline image are segmented by employing a projection along a horizontal axis of the baseline image to identify positions of the characters. The characters are classified based on a statistical classifier to obtain a confidence score for the probability of properly identifying each character. The above steps are recursively performed until each confidence score exceeds a threshold value to recognize the characters.
In alternate methods, the step of detecting orientation, position, and illumination conditions of the image and accounting for the orientations, position, and illumination conditions of the image to obtain a baseline image of the license plate may include the step of comparing each character in the image of the license plate with examples of images with different illuminations to account for illumination effects on the image. The step of detecting a region in which the license plate is located by performing a coarse localization of the image may include the steps of sub-sampling the image to reduce a number of pixels, extracting vertical edges in the image, generating a saliency map based upon the vertical edges to identify regions in the image with a probability of including the license plate and extracting a localization result which includes the image of the license plate. The step of segmenting characters depicted in the baseline image by employing a projection along a horizontal axis of the baseline image to identify positions of the characters may include the steps of providing a projection profile of pixel intensities across vertical lines of pixels in the baseline image, filtering the projection profile and identifying locations of characters in the baseline image depicted by area below a threshold value in the filtered projection profile.
The statistical classifier may employ a convolutional network, and the step of classifying the characters based on a statistical classifier to obtain a confidence score for the probability of properly identifying each character may include the step of training the classifier by employing virtual samples of characters. The method may include the step of comparing character blocks and characters to predetermined license plate codes and conventions to check accuracy of recognition. The step of recursively performing the method steps until each confidence score exceeds a threshold value may include the step of considering adjacent characters together to attempt to improve the confidence score. The above method steps may be implemented by a program storage device readable by machine, tangibly embodying a program of instructions executable by the machine to perform the method steps.
An intelligent camera system for recognizing license plates, in accordance with the invention, includes a camera adapted to independently capture a license plate image and recognize the license plate image. The camera includes a processor for managing image data and executing a license plate recognition program device. The license plate recognition program device includes means for detecting orientation, position, illumination conditions and blurring of the image and accounting for the orientations, position, illumination conditions and blurring of the image to obtain a baseline image of the license plate. The camera includes means for segmenting characters depicted in the baseline image by employing a projection along a horizontal axis of the baseline image to identify positions of the characters. A statistical classifier is adapted for recognizing and classifying the characters based on a confidence score, the confidence score being based on a probability of properly identifying each character. A memory is included for storing the license plate recognition program and the license plate image taken by an image capture device of the camera.
In alternate embodiments, a trigger device may be adapted to cause an image to be captured responsive to an event. The event may include an approach of a vehicle. The means for detecting may include examples of images with different illuminations to account for illumination effects on the image for each character within the image. The means for segmenting may include means for providing a projection profile of pixel intensities across vertical lines of pixels in the baseline image, a filter profile for filtering the projection profile and means for identifying locations of characters in the baseline image depicted by area below a threshold value in the filtered projection profile. The statistical classifier may includes one of a convolutional network and a fully connected multilayer perceptron. The memory may include a database for predetermined license plate codes and conventions for checking accuracy of recognition. The intelligent camera system may include a parking lot control system coupled to the intelligent camera system for determining parking fees based on character recognition of license plates.
These and other objects, features and advantages of the present invention will become apparent from the following detailed description of illustrative embodiments thereof, which is to be read in connection with the accompanying drawings.